
import networkx as nx
import osmnx as ox
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme(color_codes=True)
import numpy as np
import pandas as pd
import folium
import contextily as ctx
import geopandas as gpd
import mplleaflet
import overpy
from pyrosm import OSM, get_data
import pandana
import networkx as nx
from pandana.loaders import osm
import warnings
warnings.filterwarnings('ignore')
import osmnet
from shapely import wkt


ctx.__version__

'1.0.0'


overpass_client = overpy.Overpass()


#walking network of Tartu
G = ox.graph_from_place("Tartu, Estonia",network_type="walk")
fig, ax = ox.plot_graph(G,node_size = 0,edge_linewidth = 0.5, edge_color = "white")


nodes, streets = ox.graph_to_gdfs(G)
streets.head()


#EPSG:3301 - Estonian Coordinate System of 1997
nodes_3301=nodes.to_crs("EPSG:3301")
streets_3301=streets.to_crs("EPSG:3301")


#walking network with basemap
fig,ax = plt.subplots(1,1, figsize=(15,15))
streets_3301.plot(ax=ax,linewidth=0.4,color="gray")
plt.grid(False)
ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")


tags = {'amenity': True}
POI = ox.geometries_from_place('Tartu, Estonia', tags)

POI


POI.reset_index()["element_type"].unique()

array(['node', 'way', 'relation'], dtype=object)


POI_3301=POI.to_crs("EPSG:3301")


#calculate centroids for simlicity
POI_3301["geometry"] = POI_3301.centroid
POI["geometry"] = POI.centroid

C:\Users\Kasutaja\AppData\Local\Temp\ipykernel_964\1575554792.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  POI["geometry"] = POI.centroid


#map of all POI in Tartu
fig,ax = plt.subplots(1,1, figsize=(15,15))
POI_3301.plot(ax=ax,alpha=0.3,color="blue",markersize=100)
plt.grid(False)
ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\geopandas\plotting.py:74: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if mpl >= LooseVersion("3.4") or (mpl > LooseVersion("3.3.2") and "+" in mpl):
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\setuptools\_distutils\version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  other = LooseVersion(other)
C:\Users\Kasutaja\AppData\Local\Temp\ipykernel_964\2086473233.py:4: FutureWarning: The "url" option is deprecated. Please use the "source" argument instead.
  ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\contextily\tile.py:352: RasterioDeprecationWarning: Source dataset should be opened in read-only mode. Use of datasets opened in modes other than 'r' will be disallowed in a future version.
  w_img, vrt = _warper(


#POI and walking network with basemap
fig,ax = plt.subplots(1,1, figsize=(15,15))
POI_3301.plot(ax=ax,alpha=0.3,color="blue",zorder=2,markersize=100)
streets_3301.plot(ax=ax,linewidth=0.4,color="gray",zorder=1)
plt.grid(False)
ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\geopandas\plotting.py:74: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if mpl >= LooseVersion("3.4") or (mpl > LooseVersion("3.3.2") and "+" in mpl):
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\setuptools\_distutils\version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  other = LooseVersion(other)
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\geopandas\plotting.py:74: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if mpl >= LooseVersion("3.4") or (mpl > LooseVersion("3.3.2") and "+" in mpl):
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\setuptools\_distutils\version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  other = LooseVersion(other)
C:\Users\Kasutaja\AppData\Local\Temp\ipykernel_964\3439086888.py:5: FutureWarning: The "url" option is deprecated. Please use the "source" argument instead.
  ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\contextily\tile.py:352: RasterioDeprecationWarning: Source dataset should be opened in read-only mode. Use of datasets opened in modes other than 'r' will be disallowed in a future version.
  w_img, vrt = _warper(


#network from box covering Tartu
network = osm.pdna_network_from_bbox(58.330865,26.62424,58.427945,26.836414)

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\osmnet\load.py:445: FutureWarning: Assigning CRS to a GeoDataFrame without a geometry column is now deprecated and will not be supported in the future.
  gdf.crs = crs
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\pyproj\crs\crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6
  in_crs_string = _prepare_from_proj_string(in_crs_string)
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\osmnet\load.py:445: FutureWarning: Assigning CRS to a GeoDataFrame without a geometry column is now deprecated and will not be supported in the future.
  gdf.crs = crs
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\osmnet\load.py:147: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.
  'in {:,} request(s)'.format(len(geometry)))
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\osmnet\load.py:151: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
  for poly in geometry:

Requesting network data within bounding box from Overpass API in 1 request(s)
Posting to http://www.overpass-api.de/api/interpreter with timeout=180, "{'data': '[out:json][timeout:180];(way["highway"]["highway"!~"motor|proposed|construction|abandoned|platform|raceway"]["foot"!~"no"]["pedestrians"!~"no"](58.33086500,26.62424000,58.42794500,26.83641400);>;);out;'}"

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\osmnet\load.py:237: DeprecationWarning: Flags not at the start of the expression '//(?s)(.*?)/'
  domain = re.findall(r'//(?s)(.*?)/', url)[0]
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\osmnet\load.py:171: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.
  ' {:,.2f} seconds'.format(len(geometry), time.time()-start_time))

Downloaded 7,363.9KB from www.overpass-api.de in 2.54 seconds
Downloaded OSM network data within bounding box from Overpass API in 1 request(s) and 2.70 seconds
Returning OSM data with 52,469 nodes and 11,167 ways...
Edge node pairs completed. Took 15.24 seconds
Returning processed graph with 16,181 nodes and 23,784 edges...
Completed OSM data download and Pandana node and edge table creation in 18.91 seconds


network.edges_df


network.nodes_df


network = pandana.Network(network.nodes_df['x'], network.nodes_df['y'], 
                          network.edges_df['from'], network.edges_df['to'], network.edges_df[['distance']])


network.set_pois(category = 'POI',
                 maxdist = 100000,
                 maxitems = len(network.nodes_df),
                 x_col = POI["geometry"].reset_index()["geometry"].x, 
                 y_col = POI["geometry"].reset_index()["geometry"].y)

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\pandana\network.py:660: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  elif isinstance(maxitems, type(pd.Series())):
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\pandana\network.py:668: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  elif isinstance(maxdist, type(pd.Series())):


#calculate distance from each intersection to 5 nearest POI
results = network.nearest_pois(distance = 100000,
                               category = 'POI',
                               num_pois = 5,
                               include_poi_ids = True)
results


tartu_borders=gpd.read_file("tartu_borders.shp")
tartu_borders.crs

<Derived Projected CRS: EPSG:3301>
Name: Estonian Coordinate System of 1997
Axis Info [cartesian]:
- X[north]: Northing (metre)
- Y[east]: Easting (metre)
Area of Use:
- name: Estonia - onshore and offshore.
- bounds: (20.37, 57.52, 28.2, 60.0)
Coordinate Operation:
- name: Estonian National Grid
- method: Lambert Conic Conformal (2SP)
Datum: Estonia 1997
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich


nodes_gdf = gpd.GeoDataFrame(
    network.nodes_df, geometry=gpd.points_from_xy(network.nodes_df.x, network.nodes_df.y),crs="EPSG:4326")


nodes_3301=nodes_gdf.to_crs("EPSG:3301")
nodes_3301


nodes_tartu=gpd.sjoin(nodes_3301,tartu_borders,op="within")
nodes_tartu

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\IPython\core\interactiveshell.py:3301: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.
  if await self.run_code(code, result, async_=asy):


#leave points that were within Tartu's borders
results_tartu=results.loc[nodes_tartu.index]
results_tartu


tags = {'amenity': 'restaurant'}
restaurants = ox.geometries_from_place('Tartu, Estonia', tags)
restaurants

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\osmnx\geocoder.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  gdf = gdf.append(_geocode_query_to_gdf(q, wr, by_osmid))


restaurants["geometry"] = restaurants.centroid

C:\Users\Kasutaja\AppData\Local\Temp\ipykernel_964\2482192568.py:1: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  restaurants["geometry"] = restaurants.centroid


network.set_pois(category = 'restaurant',
                 maxdist = 100000,
                 maxitems = len(network.nodes_df),
                 x_col = restaurants["geometry"].reset_index()["geometry"].x, 
                 y_col = restaurants["geometry"].reset_index()["geometry"].y)

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\pandana\network.py:660: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  elif isinstance(maxitems, type(pd.Series())):
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\pandana\network.py:668: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  elif isinstance(maxdist, type(pd.Series())):


restaurants_accessibility = network.nearest_pois(distance = 100000,
                               category = 'restaurant',
                               num_pois = 5,
                               include_poi_ids = True)
restaurants_accessibility


#distance from intersections to 5 nearest restaurants in Tartu
restaurants_tartu=restaurants_accessibility.loc[nodes_tartu.index]
restaurants_tartu


restaurants_tartu_coord=pd.concat([restaurants_tartu,nodes_tartu[["x","y"]]],axis=1)
restaurants_tartu_coord


restaurants_gdf = gpd.GeoDataFrame(
    restaurants_tartu_coord, geometry=gpd.points_from_xy(restaurants_tartu_coord.x, restaurants_tartu_coord.y),crs="EPSG:4326")
restaurants_gdf


restaurants_3301=restaurants_gdf.to_crs("EPSG:3301")


#Walking distance to the first nearest restaurant from each node
fig,ax=plt.subplots(1,1,figsize=(12,12))
restaurants_3301.plot(ax=ax,c=restaurants_3301[1],markersize=10)
sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=min(restaurants_3301[1]), vmax=max(restaurants_3301[1])))
cbar = fig.colorbar(sm,fraction=0.046, pad=0.04)
cbar.set_label('Walking distance to the first nearest restaurant from each node',fontdict={'fontsize': '14', 'fontweight' : '3'})
plt.grid(False)
ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\geopandas\plotting.py:74: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if mpl >= LooseVersion("3.4") or (mpl > LooseVersion("3.3.2") and "+" in mpl):
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\setuptools\_distutils\version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  other = LooseVersion(other)
C:\Users\Kasutaja\AppData\Local\Temp\ipykernel_964\3527973734.py:4: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
  cbar = fig.colorbar(sm,fraction=0.046, pad=0.04)
C:\Users\Kasutaja\AppData\Local\Temp\ipykernel_964\3527973734.py:7: FutureWarning: The "url" option is deprecated. Please use the "source" argument instead.
  ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\contextily\tile.py:352: RasterioDeprecationWarning: Source dataset should be opened in read-only mode. Use of datasets opened in modes other than 'r' will be disallowed in a future version.
  w_img, vrt = _warper(


#Walking distance to the fifth nearest restaurant from each node
fig,ax=plt.subplots(1,1,figsize=(12,12))
restaurants_3301.plot(ax=ax,c=restaurants_3301[5],markersize=10)
sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=min(restaurants_3301[5]), vmax=max(restaurants_3301[5])))
cbar.set_label('Walking distance to the fifth nearest restaurant from each node',fontdict={'fontsize': '14', 'fontweight' : '3'})
cbar = fig.colorbar(sm,fraction=0.046, pad=0.04)
plt.grid(False)
ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\geopandas\plotting.py:74: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if mpl >= LooseVersion("3.4") or (mpl > LooseVersion("3.3.2") and "+" in mpl):
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\setuptools\_distutils\version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  other = LooseVersion(other)
C:\Users\Kasutaja\AppData\Local\Temp\ipykernel_964\1755466333.py:5: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
  cbar = fig.colorbar(sm,fraction=0.046, pad=0.04)
C:\Users\Kasutaja\AppData\Local\Temp\ipykernel_964\1755466333.py:7: FutureWarning: The "url" option is deprecated. Please use the "source" argument instead.
  ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\contextily\tile.py:352: RasterioDeprecationWarning: Source dataset should be opened in read-only mode. Use of datasets opened in modes other than 'r' will be disallowed in a future version.
  w_img, vrt = _warper(


nearest_POI_tartu=gpd.GeoDataFrame(pd.concat([results_tartu[[1]],nodes_tartu],axis=1),geometry="geometry")
nearest_POI_tartu


#Tartu's neighbourhoods
tartu_neighbourhoods=ox.geometries.geometries_from_place("Tartu",tags={'place':'suburb'})
tartu_neighbourhoods=tartu_neighbourhoods.to_crs("EPSG:3301")
tartu_neighbourhoods_boundary=tartu_neighbourhoods.boundary.to_crs("EPSG:3301")

fig,ax=plt.subplots(1,1,figsize=(12,12))
tartu_neighbourhoods.plot(ax=ax,alpha=0.1,color='blue')
tartu_neighbourhoods_boundary.plot(ax=ax,color='black')
ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")
plt.grid(False)

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\osmnx\geocoder.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  gdf = gdf.append(_geocode_query_to_gdf(q, wr, by_osmid))
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\geopandas\plotting.py:74: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if mpl >= LooseVersion("3.4") or (mpl > LooseVersion("3.3.2") and "+" in mpl):
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\setuptools\_distutils\version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  other = LooseVersion(other)
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\geopandas\plotting.py:74: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if mpl >= LooseVersion("3.4") or (mpl > LooseVersion("3.3.2") and "+" in mpl):
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\setuptools\_distutils\version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  other = LooseVersion(other)
C:\Users\Kasutaja\AppData\Local\Temp\ipykernel_964\2277575871.py:7: FutureWarning: The "url" option is deprecated. Please use the "source" argument instead.
  ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\contextily\tile.py:352: RasterioDeprecationWarning: Source dataset should be opened in read-only mode. Use of datasets opened in modes other than 'r' will be disallowed in a future version.
  w_img, vrt = _warper(


tartu_neighbourhoods


nearest_POI_tartu=nearest_POI_tartu.drop(columns=["index_right"])


POI_neighbours=gpd.sjoin(nearest_POI_tartu,tartu_neighbourhoods.reset_index(),op="within").reset_index().drop_duplicates(["id"],keep="last")
POI_neighbours

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\IPython\core\interactiveshell.py:3301: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.
  if await self.run_code(code, result, async_=asy):


#mean distance to POI in each neighbourhood
POI_neighbours.groupby(["name"]).mean()[1]

name
Annelinn                91.083743
Ihaste                 741.080850
Jaamamõisa             180.759550
Karlova                 95.197316
Kesklinn                13.892000
Maarjamõisa             92.741402
Raadi-Kruusamäe        174.850895
Riiamäe                 39.233777
Ropka                  122.496258
Ropka tööstusrajoon    165.864754
Ränilinn               131.856231
Supilinn               233.438086
Tammelinn              182.783142
Toometaguse             49.551809
Tähtvere                98.924331
Uueturu                 30.227419
Vaksali                 46.168410
Vanalinn                71.596998
Variku                 550.352013
Veeriku                174.000833
Ülejõe                  95.760014
Name: 1, dtype: float64


tartu_neighbourhoods_POI_distance=tartu_neighbourhoods.merge(POI_neighbours.groupby(["name"]).mean()[1],left_on='name',right_on='name')


tartu_neighbourhoods_POI_distance["time"]=(tartu_neighbourhoods_POI_distance[1]/1.39/60).apply(lambda x: round(x,2))


tartu_neighbourhoods_POI_distance["time"]

0     2.17
1     2.10
2     1.58
3     2.80
4     1.15
5     1.11
6     6.60
7     2.19
8     2.09
9     1.19
10    0.55
11    1.47
12    0.17
13    1.14
14    1.09
15    8.89
16    1.99
17    0.36
18    0.47
19    0.59
20    0.86
Name: time, dtype: float64


#let's assume that average walking speed is 5 km/h or 1.39 m/s
fig,ax=plt.subplots(1,1,figsize=(12,12))
tartu_neighbourhoods_POI_distance.plot(ax=ax,alpha=0.5,column="time",cmap="Blues", scheme="User_Defined",
                                      legend=True,classification_kwds=dict(bins=[1,2,3,7]))
tartu_neighbourhoods_boundary.plot(ax=ax,color='black')
ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")
plt.grid(False)
plt.title("Walkable neighbourhoods in Tartu",fontsize='18')

C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\geopandas\plotting.py:740: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if mapclassify.__version__ < LooseVersion("2.4.0"):
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\setuptools\_distutils\version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  other = LooseVersion(other)
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\geopandas\plotting.py:74: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if mpl >= LooseVersion("3.4") or (mpl > LooseVersion("3.3.2") and "+" in mpl):
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\setuptools\_distutils\version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  other = LooseVersion(other)
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\geopandas\plotting.py:74: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if mpl >= LooseVersion("3.4") or (mpl > LooseVersion("3.3.2") and "+" in mpl):
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\setuptools\_distutils\version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  other = LooseVersion(other)
C:\Users\Kasutaja\AppData\Local\Temp\ipykernel_964\3799305427.py:6: FutureWarning: The "url" option is deprecated. Please use the "source" argument instead.
  ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3301")
C:\Users\Kasutaja\anaconda3\envs\geopandas\lib\site-packages\contextily\tile.py:352: RasterioDeprecationWarning: Source dataset should be opened in read-only mode. Use of datasets opened in modes other than 'r' will be disallowed in a future version.
  w_img, vrt = _warper(

Text(0.5, 1.0, 'Walkable neighbourhoods in Tartu')


#some data preprocessing and loading
df_taxi=pd.read_csv("yellow_tripdata_2020-01.csv")
df_taxi

KeyboardInterrupt


df_taxi=df_taxi.dropna()


df_taxi


#check data types
df_taxi.dtypes

VendorID                 float64
tpep_pickup_datetime      object
tpep_dropoff_datetime     object
passenger_count          float64
trip_distance            float64
RatecodeID               float64
store_and_fwd_flag        object
PULocationID               int64
DOLocationID               int64
payment_type             float64
fare_amount              float64
extra                    float64
mta_tax                  float64
tip_amount               float64
tolls_amount             float64
improvement_surcharge    float64
total_amount             float64
congestion_surcharge     float64
dtype: object


taxi_zones=pd.read_csv("taxi_zones.csv")
taxi_zones


taxi_zones['geometry'] = taxi_zones.the_geom.apply(wkt.loads)


taxi_zones=taxi_zones.drop(columns=["the_geom"])


taxi_zones_gdf=gpd.GeoDataFrame(taxi_zones,geometry="geometry",crs="EPSG:4326")
taxi_zones_gdf


#taxi zones on the map
taxi_zones_3857=taxi_zones_gdf.to_crs("EPSG:3857")
taxi_zones_3857_boundary=taxi_zones_3857.boundary.to_crs("EPSG:3857")
fig,ax=plt.subplots(1,1,figsize=(12,12))
taxi_zones_3857.plot(ax=ax,alpha=0.1,color='blue')
taxi_zones_3857_boundary.plot(ax=ax,color='black',linewidth=0.25)
ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3857")
plt.grid(False)


#turn zones to centroids
taxi_zones_3857["geometry"]=taxi_zones_3857.centroid
taxi_zones_3857


from sklearn.utils import shuffle
df_taxi = shuffle(df_taxi,random_state=0)


df_taxi=df_taxi.iloc[0:5000].reset_index().drop(columns=["index"])


#split taxi dataset to 2 datasets: 1 with start point, the other one with destination point
df_taxi_start=gpd.GeoDataFrame(df_taxi.merge(taxi_zones_3857, left_on="PULocationID",right_on="LocationID"),geometry="geometry",crs="EPSG:3857")
df_taxi_end=gpd.GeoDataFrame(df_taxi.merge(taxi_zones_3857, left_on="DOLocationID",right_on="LocationID"),geometry="geometry",crs="EPSG:3857")


df_taxi_start_4326=df_taxi_start.to_crs("EPSG:4326")
df_taxi_end_4326=df_taxi_end.to_crs("EPSG:4326")


df_taxi_start["x"]=df_taxi_start["geometry"].x
df_taxi_start["y"]=df_taxi_start["geometry"].y
df_taxi_end["x"]=df_taxi_end["geometry"].x
df_taxi_end["y"]=df_taxi_end["geometry"].y


points_start=df_taxi_start[["x","y"]].values.tolist()


points_end=df_taxi_end[["x","y"]].values.tolist()


from pointpats import PointPattern, PoissonPointProcess, as_window, G, F, J, K, L, Genv, Fenv, Jenv, Kenv, Lenv


import pointpats
pointpats.__version__

'2.1.0'


pp_start = PointPattern(points_start)
pp_end = PointPattern(points_end)


#I used L function to identify whether the data is clustered or not
#values above 0 indicate clustering, while below 0 show that data is dispersed https://mgimond.github.io/Spatial/chp11_0.html
#According to plots, start and end points are a little bit clustered until 5000m, but afterwards they become more and more
#dispersed
#manual - https://nbviewer.org/github/pysal/pointpats/blob/master/notebooks/distance_statistics.ipynb


lp_start = L(pp_start)
lp_start.plot()


lp_end = L(pp_end)
lp_end.plot()


taxi_zones=pd.read_csv("taxi_zones.csv")
taxi_zones['geometry'] = taxi_zones.the_geom.apply(wkt.loads)
taxi_zones=taxi_zones.drop(columns=["the_geom"])
taxi_zones_gdf=gpd.GeoDataFrame(taxi_zones,geometry="geometry",crs="EPSG:4326")
taxi_zones_3857=taxi_zones_gdf.to_crs("EPSG:3857")


df_taxi=pd.read_csv("yellow_tripdata_2020-01.csv")
df_taxi=df_taxi.dropna()
df_taxi["tpep_pickup_datetime"]=pd.to_datetime(df_taxi["tpep_pickup_datetime"])
df_taxi["tpep_dropoff_datetime"]=pd.to_datetime(df_taxi["tpep_dropoff_datetime"])
df_taxi_start=gpd.GeoDataFrame(df_taxi.merge(taxi_zones_3857, left_on="PULocationID",right_on="LocationID"),geometry="geometry",crs="EPSG:3857")
df_taxi_end=gpd.GeoDataFrame(df_taxi.merge(taxi_zones_3857, left_on="DOLocationID",right_on="LocationID"),geometry="geometry",crs="EPSG:3857")


df_taxi_start["hour"]=df_taxi_start["tpep_pickup_datetime"].dt.hour
df_taxi_end["hour"]=df_taxi_start["tpep_dropoff_datetime"].dt.hour


#let's assume that night time is from 0 until 5 a.m.
df_taxi_start["is_night"]=((df_taxi_start["hour"]>0) & (df_taxi_start["hour"]<5)).astype("int")
df_taxi_end["is_night"]=((df_taxi_end["hour"]>0) & (df_taxi_end["hour"]<5)).astype("int")


df_taxi_start=df_taxi_start.groupby("PULocationID").sum().reset_index()


df_taxi_end=df_taxi_end.groupby("DOLocationID").sum().reset_index()


df_taxi_start=df_taxi_start.merge(taxi_zones_3857[["geometry","LocationID"]],left_on="PULocationID",right_on="LocationID")
df_taxi_end=df_taxi_end.merge(taxi_zones_3857[["geometry","LocationID"]],left_on="DOLocationID",right_on="LocationID")


df_taxi_start=gpd.GeoDataFrame(df_taxi_start,geometry="geometry",crs="EPSG:4326")
df_taxi_end=gpd.GeoDataFrame(df_taxi_end,geometry="geometry",crs="EPSG:4326")


df_taxi_start=df_taxi_start[df_taxi_start["is_night"]>0]
df_taxi_end=df_taxi_end[df_taxi_end["is_night"]>0]


#spatial weights

import libpysal as lps

wq_start =  lps.weights.Queen.from_dataframe(df_taxi_start)
wq_end =  lps.weights.Queen.from_dataframe(df_taxi_end)


#Moran’s I test for global autocorrelation


wq_start.transform = 'r'
wq_end.transform = 'r'

('WARNING: ', 0, ' is an island (no neighbors)')
('WARNING: ', 40, ' is an island (no neighbors)')
('WARNING: ', 181, ' is an island (no neighbors)')
('WARNING: ', 0, ' is an island (no neighbors)')
('WARNING: ', 30, ' is an island (no neighbors)')
('WARNING: ', 38, ' is an island (no neighbors)')
('WARNING: ', 69, ' is an island (no neighbors)')


df_taxi_start=df_taxi_start.reset_index().drop(columns=["index"]).drop(index=[0,40,181]).reset_index().drop(columns=["index"])
df_taxi_end=df_taxi_end.reset_index().drop(columns=["index"]).drop(index=[0,30,38,69]).reset_index().drop(columns=["index"])
wq_start =  lps.weights.Queen.from_dataframe(df_taxi_start)
wq_end =  lps.weights.Queen.from_dataframe(df_taxi_end)
wq_start.transform = 'r'
wq_end.transform = 'r'


import esda
mi_start = esda.moran.Moran(df_taxi_start["is_night"], wq_start)
mi_end = esda.moran.Moran(df_taxi_end["is_night"], wq_end)


print(mi_start.I)
print(mi_end.I)

0.5572768987741263
0.4937483237990811


import seaborn as sbn
sbn.kdeplot(mi_start.sim, shade=True)
plt.vlines(mi_start.I, 0, 1, color='r')
plt.vlines(mi_start.EI, 0,1)
plt.xlabel("Moran's I")

Text(0.5, 0, "Moran's I")


mi_start.p_sim

0.001


import seaborn as sbn
sbn.kdeplot(mi_end.sim, shade=True)
plt.vlines(mi_end.I, 0, 1, color='r')
plt.vlines(mi_end.EI, 0,1)
plt.xlabel("Moran's I")

Text(0.5, 0, "Moran's I")


mi_end.p_sim

0.001


#statistical tests and plots above demonstrate that there is spatial autocorrelation


#Let’s compute a local Moran statistic
wq_start.transform = 'r'
wq_end.transform = 'r'


li_start = esda.moran.Moran_Local(df_taxi_start["is_night"], wq_start, transformation = "r")
li_end = esda.moran.Moran_Local(df_taxi_end["is_night"], wq_end, transformation = "r")


np.unique(li_start.q,return_counts=True)

(array([1, 2, 3, 4]), array([ 39,  11, 181,   3], dtype=int64))


sig_start = li_start.p_sim < 0.05
hotspot_start = sig_start * li_start.q==1
sig_end = li_end.p_sim < 0.05
hotspot_end = sig_end * li_end.q==1


df_taxi_start["is_hotspot"]=hotspot_start
df_taxi_end["is_hotspot"]=hotspot_end


df_taxi_start_boundary=df_taxi_start.boundary.to_crs("EPSG:3857")
df_taxi_end_boundary=df_taxi_end.boundary.to_crs("EPSG:3857")


fig,ax=plt.subplots(1,1,figsize=(12,12))
df_taxi_start[df_taxi_start["is_hotspot"]].plot(ax=ax,color="red",alpha=0.5)
df_taxi_start_boundary.plot(ax=ax,color='black',linewidth=0.25)
taxi_zones_3857.plot(ax=ax,alpha=0.1,color='blue')
taxi_zones_3857.boundary.plot(ax=ax,color='black',linewidth=0.25)
ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3857")
plt.grid(False)
plt.title("Hotspots of starting points",fontsize=16)

Text(0.5, 1.0, 'Hotspots of starting points')


fig,ax=plt.subplots(1,1,figsize=(12,12))
df_taxi_end[df_taxi_end["is_hotspot"]].plot(ax=ax,color="red",alpha=0.5)
df_taxi_end_boundary.plot(ax=ax,color='black',linewidth=0.25)
taxi_zones_3857.plot(ax=ax,alpha=0.1,color='blue')
taxi_zones_3857.boundary.plot(ax=ax,color='black',linewidth=0.25)
ctx.add_basemap(ax=ax, url=ctx.providers.OpenStreetMap.Mapnik, crs="EPSG:3857")
plt.grid(False)
plt.title("Hotspots of ending points",fontsize=16)

Text(0.5, 1.0, 'Hotspots of ending points')

			osmid	oneway	lanes	ref	name	highway	maxspeed	length	geometry	bridge	junction	service	access	tunnel	width
u	v	key
8220933	6854135436	0	117178126	False	5	3	Riia	trunk	50	65.343	LINESTRING (26.73016 58.37857, 26.72998 58.378...	NaN	NaN	NaN	NaN	NaN	NaN
	8220941	0	[731962213, 730852870, 29394711]	False	4	3	[Riia, Narva mnt]	trunk	50	137.133	LINESTRING (26.73016 58.37857, 26.73027 58.378...	yes	NaN	NaN	NaN	NaN	NaN
	6854135407	0	731962221	False	3	3	Riia	trunk	50	39.572	LINESTRING (26.73016 58.37857, 26.73003 58.378...	NaN	NaN	NaN	NaN	NaN	NaN
8220941	332221897	0	28906993	False	2	3	Narva mnt	trunk	40	55.405	LINESTRING (26.73056 58.37979, 26.73061 58.379...	NaN	NaN	NaN	NaN	NaN	NaN
8220941	332221894	0	29394713	False	2	NaN	NaN	tertiary_link	NaN	56.966	LINESTRING (26.73056 58.37979, 26.73068 58.380...	NaN	NaN	NaN	NaN	NaN	NaN

		addr:city	addr:country	addr:housenumber	addr:postcode	addr:street	amenity	email	healthcare	name	opening_hours	...	payment:prepaid_cards	payment:token	fuel:taxfree_diesel	toilets	shower	vacuum_cleaner	vacuum_cleaner:self_service	note	ways	type
element_type	osmid
node	322920315	Tartu	EE	31	50417	Raja	pharmacy	haru@apteek.ee	pharmacy	Raja Apteek	Mo-Th 08:00-17:30; Fr 08:00-16:00	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	322943595	Tartu	EE	20	51009	Raatuse	pharmacy	raatuse@uusapteek.ee	NaN	Raatuse Apteek	Mo-Fr 09:00-20:00; Sa 10:00-18:00	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	323377901	NaN	NaN	NaN	NaN	NaN	post_box	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	331179291	NaN	NaN	NaN	NaN	NaN	pub	NaN	NaN	Zavood	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	332950268	Tartu	EE	8	51004	Poe	pharmacy	kesklinnaapt@ut.ee	pharmacy	Tartu Ülikooli Kesklinna Apteek	Mo-Fr 08:00-20:00; Sa 09:00-17:00	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
way	1021163496	NaN	NaN	NaN	NaN	NaN	parking	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
way	1038982213	NaN	NaN	NaN	NaN	NaN	school	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
relation	6740798	NaN	NaN	NaN	NaN	NaN	parking	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	[455823164, 30230476]	multipolygon
	11633951	NaN	NaN	NaN	NaN	NaN	university	NaN	NaN	Õigusteaduskond	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	[686122978, 686122980]	multipolygon
	12767188	NaN	NaN	NaN	NaN	NaN	parking	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	[948074365, 948074366]	multipolygon

		from	to	distance
8220933	8220937	8220933	8220937	55.883880
8220933	6854135436	8220933	6854135436	65.315136
8220937	6845284091	8220937	6845284091	58.194957
8220941	332221894	8220941	332221894	55.681436
8220941	332221897	8220941	332221897	55.117052
...	...	...	...	...
9634231486	9634231484	9634231486	9634231484	35.334529
9694890969	330042607	9694890969	330042607	27.211448
9694890969	9694890974	9694890969	9694890974	20.902787
9694890973	8157535477	9694890973	8157535477	82.103047
9694890974	9694890973	9694890974	9694890973	9.079948

	x	y
id
8220933	26.730165	58.378573
8220937	26.730356	58.379065
8220941	26.730560	58.379786
8220974	26.726640	58.386388
8221115	26.728486	58.389628
...	...	...
9634231484	26.725708	58.413244
9634231486	26.726314	58.413233
9694890969	26.741194	58.366988
9694890973	26.741233	58.366719
9694890974	26.741221	58.366800

	1	2	3	4	5	poi1	poi2	poi3	poi4	poi5
id
8220933	119.190002	131.156006	131.580002	143.690002	143.690002	362.0	366.0	147.0	423.0	906.0
8220937	175.072998	187.039001	187.462997	199.572998	199.572998	362.0	366.0	147.0	423.0	906.0
8220941	162.701004	170.119003	192.936005	227.399002	256.127991	1007.0	1006.0	1005.0	215.0	362.0
8220974	50.122002	83.512001	83.512001	89.620003	89.620003	1510.0	841.0	1512.0	838.0	940.0
8221115	76.574997	136.216995	222.897003	235.731003	292.247009	944.0	1432.0	448.0	919.0	207.0
...	...	...	...	...	...	...	...	...	...	...
9634231484	1877.687012	1905.285034	1978.928955	2092.269043	2131.999023	450.0	1383.0	678.0	892.0	1382.0
9634231486	1913.020996	1940.619019	2014.262939	2127.603027	2167.333008	450.0	1383.0	678.0	892.0	1382.0
9694890969	62.842999	62.842999	186.679993	186.679993	199.582001	380.0	1216.0	125.0	1163.0	128.0
9694890973	92.823997	92.823997	191.072998	216.660995	216.660995	380.0	1216.0	128.0	125.0	1163.0
9694890974	83.745003	83.745003	200.151993	207.582001	207.582001	380.0	1216.0	128.0	125.0	1163.0

Road network of Tartu (walking network)¶

Points of interest in Tartu¶

POIs and Road network¶

Distances between the POIs and road network intersection nodes¶

Accessibility map for walking distance to the restaurants¶

Accessibility map representing walkable neighborhoods in Tartu¶

--------------------------------------------- Task 2 ------------------------------------------------------¶

Point pattern analysis based on a distance-based approach to identify nature of the data¶

Unfortunately, my PC can't handle with such big dataset, I tried different methods (including StratifiedKFold from sklearn) to reduce the size and save the same data's distribution, but it also didn't work because of memory issues. I will use 5000 rows from shuffled dataset

Hotspots for nightlife activities in NYC¶

	1	2	3	4	5	poi1	poi2	poi3	poi4	poi5
id
8220933	143.690002	148.608994	250.028000	260.791992	274.545990	40.0	47.0	11.0	37.0	26.0
8220937	199.572998	204.492004	305.911011	316.674988	330.428986	40.0	47.0	11.0	37.0	26.0
8220941	280.627991	285.546997	316.713013	386.966003	397.730011	40.0	47.0	18.0	11.0	37.0
8220974	500.152008	655.463013	746.978027	750.039001	764.760010	41.0	7.0	25.0	2.0	3.0
8221115	656.786011	827.901978	1007.911987	1099.427002	1117.208984	36.0	41.0	7.0	25.0	3.0
...	...	...	...	...	...	...	...	...	...	...
9634231484	4224.079102	4332.294922	4526.201172	4617.715820	4619.477051	36.0	41.0	7.0	25.0	42.0
9634231486	4259.413086	4367.628906	4561.535156	4653.049805	4654.811035	36.0	41.0	7.0	25.0	42.0
9694890969	186.679993	430.997986	565.523010	1176.991943	1334.911987	22.0	46.0	15.0	39.0	19.0
9694890973	216.660995	401.016998	595.504028	1206.973022	1364.892944	22.0	46.0	15.0	39.0	19.0
9694890974	207.582001	410.096008	586.424988	1197.894043	1355.813965	22.0	46.0	15.0	39.0	19.0

	id	1	x	y	geometry	ANIMI	AKOOD	TYYP	ONIMI	OKOOD	...	osmid	nodes	ways	name	place	type	wikidata	wikipedia	name:de	population
113	330045927	13.892000	26.725527	58.380022	POINT (659404.532 6474299.755)	Tartu linn	8151	5	Tartu linn	0793	...	4572218	[[[3338415830, 3338415831, 3338415832, 3338415...	[327143241, 327079671, 327104771, 327150439, 3...	Kesklinn	suburb	multipolygon	Q6395101	et:Kesklinn (Tartu)	NaN	6465
1658	8220933	119.190002	26.730165	58.378573	POINT (659682.195 6474149.521)	Tartu linn	8151	5	Tartu linn	0793	...	12524099	[[[3338211031, 3338211027, 3338211026, 3338211...	[327104771, 327150439, 924853287, 925610911, 9...	Uueturu	suburb	multipolygon	Q106356210	NaN	NaN	NaN
1659	8220937	175.072998	26.730356	58.379065	POINT (659691.158 6474204.763)	Tartu linn	8151	5	Tartu linn	0793	...	12524099	[[[3338211031, 3338211027, 3338211026, 3338211...	[327104771, 327150439, 924853287, 925610911, 9...	Uueturu	suburb	multipolygon	Q106356210	NaN	NaN	NaN
1660	330036518	28.257000	26.723765	58.378670	POINT (659307.629 6474145.053)	Tartu linn	8151	5	Tartu linn	0793	...	12524099	[[[3338211031, 3338211027, 3338211026, 3338211...	[327104771, 327150439, 924853287, 925610911, 9...	Uueturu	suburb	multipolygon	Q106356210	NaN	NaN	NaN
1661	330036519	0.000000	26.725232	58.378327	POINT (659394.944 6474110.445)	Tartu linn	8151	5	Tartu linn	0793	...	12524099	[[[3338211031, 3338211027, 3338211026, 3338211...	[327104771, 327150439, 924853287, 925610911, 9...	Uueturu	suburb	multipolygon	Q106356210	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
14711	9273834508	365.619995	26.713578	58.385768	POINT (658680.179 6474910.887)	Tartu linn	8151	5	Tartu linn	0793	...	4570553	[[[3337916808, 3337916807, 3337916806, 3337916...	[327079672, 327079671, 327123287]	Supilinn	suburb	multipolygon	Q3744454	et:Supilinn	NaN	NaN
14712	9273834509	362.061005	26.713625	58.385748	POINT (658683.029 6474908.740)	Tartu linn	8151	5	Tartu linn	0793	...	4570553	[[[3337916808, 3337916807, 3337916806, 3337916...	[327079672, 327079671, 327123287]	Supilinn	suburb	multipolygon	Q3744454	et:Supilinn	NaN	NaN
14713	9273834510	379.261993	26.713402	58.385849	POINT (658669.506 6474919.440)	Tartu linn	8151	5	Tartu linn	0793	...	4570553	[[[3337916808, 3337916807, 3337916806, 3337916...	[327079672, 327079671, 327123287]	Supilinn	suburb	multipolygon	Q3744454	et:Supilinn	NaN	NaN
14714	9273834513	369.769989	26.713524	58.385793	POINT (658676.930 6474913.487)	Tartu linn	8151	5	Tartu linn	0793	...	4570553	[[[3337916808, 3337916807, 3337916806, 3337916...	[327079672, 327079671, 327123287]	Supilinn	suburb	multipolygon	Q3744454	et:Supilinn	NaN	NaN
14715	9274208408	315.050995	26.714163	58.386063	POINT (658713.018 6474945.067)	Tartu linn	8151	5	Tartu linn	0793	...	4570553	[[[3337916808, 3337916807, 3337916806, 3337916...	[327079672, 327079671, 327123287]	Supilinn	suburb	multipolygon	Q3744454	et:Supilinn	NaN	NaN

	VendorID	tpep_pickup_datetime	tpep_dropoff_datetime	passenger_count	trip_distance	RatecodeID	store_and_fwd_flag	PULocationID	DOLocationID	payment_type	fare_amount	extra	mta_tax	tip_amount	tolls_amount	improvement_surcharge	total_amount	congestion_surcharge
0	1.0	2020-01-01 00:28:15	2020-01-01 00:33:03	1.0	1.20	1.0	N	238	239	1.0	6.0	3.0	0.5	1.47	0.0	0.3	11.27	2.5
1	1.0	2020-01-01 00:35:39	2020-01-01 00:43:04	1.0	1.20	1.0	N	239	238	1.0	7.0	3.0	0.5	1.50	0.0	0.3	12.30	2.5
2	1.0	2020-01-01 00:47:41	2020-01-01 00:53:52	1.0	0.60	1.0	N	238	238	1.0	6.0	3.0	0.5	1.00	0.0	0.3	10.80	2.5
3	1.0	2020-01-01 00:55:23	2020-01-01 01:00:14	1.0	0.80	1.0	N	238	151	1.0	5.5	0.5	0.5	1.36	0.0	0.3	8.16	0.0
4	2.0	2020-01-01 00:01:58	2020-01-01 00:04:16	1.0	0.00	1.0	N	193	193	2.0	3.5	0.5	0.5	0.00	0.0	0.3	4.80	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6339562	2.0	2020-01-31 23:38:07	2020-01-31 23:52:21	1.0	2.10	1.0	N	163	246	1.0	11.0	0.5	0.5	2.96	0.0	0.3	17.76	2.5
6339563	2.0	2020-01-31 23:00:18	2020-01-31 23:19:18	1.0	2.13	1.0	N	164	79	1.0	13.0	0.5	0.5	3.36	0.0	0.3	20.16	2.5
6339564	2.0	2020-01-31 23:24:22	2020-01-31 23:40:39	1.0	2.55	1.0	N	79	68	1.0	12.5	0.5	0.5	3.26	0.0	0.3	19.56	2.5
6339565	2.0	2020-01-31 23:44:22	2020-01-31 23:54:00	1.0	1.61	1.0	N	100	142	2.0	8.5	0.5	0.5	0.00	0.0	0.3	12.30	2.5
6339566	2.0	2020-01-31 23:19:37	2020-01-31 23:20:41	1.0	0.00	1.0	N	193	193	1.0	0.0	0.0	0.0	0.00	0.0	0.0	0.00	0.0

	OBJECTID	Shape_Leng	the_geom	Shape_Area	zone	LocationID	borough
0	1	0.116357	MULTIPOLYGON (((-74.18445299999996 40.69499599...	0.000782	Newark Airport	1	EWR
1	2	0.433470	MULTIPOLYGON (((-73.82337597260663 40.63898704...	0.004866	Jamaica Bay	2	Queens
2	3	0.084341	MULTIPOLYGON (((-73.84792614099985 40.87134223...	0.000314	Allerton/Pelham Gardens	3	Bronx
3	4	0.043567	MULTIPOLYGON (((-73.97177410965318 40.72582128...	0.000112	Alphabet City	4	Manhattan
4	5	0.092146	MULTIPOLYGON (((-74.17421738099989 40.56256808...	0.000498	Arden Heights	5	Staten Island
...	...	...	...	...	...	...	...
258	256	0.067915	MULTIPOLYGON (((-73.95834207500002 40.71330630...	0.000169	Williamsburg (South Side)	256	Brooklyn
259	259	0.126750	MULTIPOLYGON (((-73.85107116191898 40.91037152...	0.000395	Woodlawn/Wakefield	259	Bronx
260	260	0.133514	MULTIPOLYGON (((-73.90175373399988 40.76077547...	0.000422	Woodside	260	Queens
261	261	0.027120	MULTIPOLYGON (((-74.01332610899988 40.70503078...	0.000034	World Trade Center	261	Manhattan
262	262	0.049064	MULTIPOLYGON (((-73.94383256699986 40.78285908...	0.000122	Yorkville East	262	Manhattan

		nodes	geometry	ways	name	place	type	wikidata	wikipedia	name:de	population
element_type	osmid
relation	4570269	[[[579708022, 3337824353, 3337824354, 33378243...	POLYGON ((660490.714 6474784.778, 660705.494 6...	[327071159, 327104774, 327072853, 45494444, 45...	Jaamamõisa	suburb	multipolygon	Q12364796	et:Jaamamõisa	NaN	NaN
	4570300	[[[3337824366, 8154384310, 3337847617, 4739253...	POLYGON ((658319.628 6476954.789, 658381.570 6...	[327072852, 45494450, 327071161, 327072853]	Raadi-Kruusamäe	suburb	multipolygon	Q7278379	et:Raadi-Kruusamäe	NaN	NaN
	4570419	[[[579737673, 3337871439, 3337871438, 33378714...	POLYGON ((656607.242 6471045.482, 656602.861 6...	[327076357, 327109026, 327076358, 45494453, 45...	Ränilinn	suburb	multipolygon	Q7386050	et:Ränilinn	NaN	NaN
	4570553	[[[3337916808, 3337916807, 3337916806, 3337916...	POLYGON ((658485.206 6475582.216, 658547.832 6...	[327079672, 327079671, 327123287]	Supilinn	suburb	multipolygon	Q3744454	et:Supilinn	NaN	NaN
	4571250	[[[579737604, 579737605, 579737606, 579737607,...	POLYGON ((657203.642 6477603.243, 657208.781 6...	[45494447, 45494451, 327072854, 327072852, 327...	Ülejõe	suburb	multipolygon	Q8079448	et:Ülejõe (Tartu)	NaN	NaN
	4571269	[[[579737567, 3338240931, 3338240933, 33382409...	POLYGON ((656711.097 6472107.181, 656696.370 6...	[327109025, 327114933, 327076357, 327076359]	Maarjamõisa	suburb	multipolygon	Q10993580	et:Maarjamõisa	NaN	NaN
	4571316	[[[3338284934, 3338284943, 3338284945, 3338284...	POLYGON ((657728.828 6470590.392, 658571.728 6...	[327113118, 327114932, 327113119]	Variku	suburb	multipolygon	Q7915901	et:Variku (Tartu)	NaN	NaN
	4571354	[[[3338240943, 3338328504, 3338328505], [33383...	POLYGON ((656973.918 6471661.119, 657066.983 6...	[327114934, 327121341, 327143240, 327113118, 3...	Tammelinn	suburb	multipolygon	Q7681541	et:Tammelinn	NaN	NaN
	4571540	[[[3338399168, 3338399167, 3338399166, 3338399...	POLYGON ((656538.201 6475296.201, 656479.391 6...	[327121342, 327123289, 327114934, 327109025, 4...	Veeriku	suburb	multipolygon	Q14956368	et:Veeriku	NaN	NaN
	4571562	[[[3338415830, 3338415831, 3338415832, 3338415...	POLYGON ((656733.465 6475959.052, 656787.557 6...	[327143241, 327123288, 327121342, 327104772, 3...	Tähtvere	suburb	multipolygon	Q3513748	et:Tähtvere	Techelfer	NaN
	4572066	[[[3338647068, 3338647067, 3338647065, 3338647...	POLYGON ((657964.029 6473920.047, 658000.794 6...	[327143239, 925599957, 327150438, 327148046, 3...	Vaksali	suburb	multipolygon	Q7909032	et:Vaksali (Tartu)	NaN	NaN
	4572171	[[[3338647044, 3338691746, 3338691736, 3338691...	POLYGON ((658789.830 6470399.337, 658739.310 6...	[327148048, 327151615, 327148047, 327114932, 3...	Ropka	suburb	multipolygon	Q7366761	et:Ropka	NaN	NaN
	4572218	[[[3338415830, 3338415831, 3338415832, 3338415...	POLYGON ((658444.279 6474155.261, 658492.885 6...	[327143241, 327079671, 327104771, 327150439, 3...	Kesklinn	suburb	multipolygon	Q6395101	et:Kesklinn (Tartu)	NaN	6465
	4572236	[[[3338708250, 294540644, 294540646, 294540648...	POLYGON ((659247.527 6472084.610, 659235.132 6...	[327151613, 327151614, 925599958, 327150438, 3...	Karlova	suburb	multipolygon	Q6372551	et:Karlova	NaN	NaN
	4572258	[[[294540679, 2721236318, 294540681, 294540685...	POLYGON ((660118.366 6474077.741, 660455.274 6...	[327153881, 327156180, 327151613, 327150439, 3...	Annelinn	suburb	multipolygon	Q1813414	et:Annelinn	NaN	NaN
	4572273	[[[3338762651, 3338790418, 3338790417, 3338790...	POLYGON ((660769.721 6471624.573, 661191.142 6...	[327156181, 327156180, 327153879, 45492531, 45...	Ihaste	suburb	multipolygon	Q5994651	et:Ihaste	NaN	NaN
	4572274	[[[3338762651, 3338790418, 3338790417, 3338790...	POLYGON ((658836.784 6470235.761, 658918.988 6...	[327156181, 158476355, 327151615, 327153880, 3...	Ropka tööstusrajoon	suburb	multipolygon	Q7366763	et:Ropka tööstusrajoon	NaN	NaN
	12524099	[[[3338211031, 3338211027, 3338211026, 3338211...	POLYGON ((659276.396 6474120.420, 659233.555 6...	[327104771, 327150439, 924853287, 925610911, 9...	Uueturu	suburb	multipolygon	Q106356210	NaN	NaN	NaN
	12532594	[[[3338708248, 3338708246, 3338708245, 3338708...	POLYGON ((658649.363 6473630.418, 659148.099 6...	[327151614, 327143239, 925610910, 925596469, 9...	Riiamäe	suburb	multipolygon	Q106356280	NaN	NaN	NaN
	12532657	[[[3337916794, 8590077465, 8590077464, 8590077...	POLYGON ((658444.279 6474155.261, 658492.885 6...	[925604151, 925604150, 925599957, 327143241]	Toometaguse	suburb	multipolygon	Q106356070	NaN	NaN	NaN
	12532774	[[[3337916802, 3338211035, 3338211032, 3338211...	POLYGON ((658747.993 6474544.936, 658815.275 6...	[925529721, 327079671, 925604151, 925596469, 9...	Vanalinn	suburb	multipolygon	Q18626275	et:Tartu vanalinn	NaN	NaN